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10 June 1994 Combined statistical regularization and experiment-design-theory-based nonlinear techniques for extended objects imaging from remotely sensed data
Yuri T. Kostenko, Yuri V. Shkvarko
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Proceedings Volume 2232, Signal Processing, Sensor Fusion, and Target Recognition III; (1994) https://doi.org/10.1117/12.177756
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States
Abstract
The aim of this presentation is to address a new theoretic approach to the problem of the development of remote sensing imaging (RSI) nonlinear techniques that exploit the idea of fusion the experiment design and statistical regularization theory-based methods for inverse problems solution optimal/suboptimal in the mixed Bayesian-regularization setting. The basic purpose of such the information fusion-based methodology is twofold, namely, to design the appropriate system- oriented finite-dimensional model of the RSI experiment in the terms of projection schemes for wavefield inversion problems, and to derive the two-stage estimation techniques that provide the optimal/suboptimal restoration of the power distribution in the environment from the limited number of the wavefield measurements. We also discuss issues concerning the available control of some additional degrees of freedom while such an RSI experiment is conducted.
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Yuri T. Kostenko and Yuri V. Shkvarko "Combined statistical regularization and experiment-design-theory-based nonlinear techniques for extended objects imaging from remotely sensed data", Proc. SPIE 2232, Signal Processing, Sensor Fusion, and Target Recognition III, (10 June 1994); https://doi.org/10.1117/12.177756
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KEYWORDS
Environmental sensing
Inverse problems
Remote sensing
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